Apparatus and method for encoding digital image data in a lossless manner

ABSTRACT

A method of losslessly compressing and encoding signals representing image information is claimed. A lossy compressed data file and a residual compressed data file are generated. When the lossy compressed data file and the residual compressed data file are combined, a lossless data file that is substantially identical to the original data file is created.

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to U.S. Provisional ApplicationSerial No. 60/302,853, filed Jul. 2, 2001, pending, which application isincorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION

[0002] I. Field of the Invention

[0003] The present invention relates to image processing andcompression. More specifically, the invention relates lossless encodingof video image and audio information in the frequency domain.

[0004] II. Description of the Related Art

[0005] Digital picture processing has a prominent position in thegeneral discipline of digital signal processing. The importance of humanvisual perception has encouraged tremendous interest and advances in theart and science of digital picture processing. In the field oftransmission and reception of video signals, such as those used forprojecting films or movies, various improvements are being made to imagecompression techniques. Many of the current and proposed video systemsmake use of digital encoding techniques. Aspects of this field includeimage coding, image restoration, and image feature selection. Imagecoding represents the attempts to transmit pictures of digitalcommunication channels in an efficient manner, making use of as few bitsas possible to minimize the band width required, while at the same time,maintaining distortions within certain limits. Image restorationrepresents efforts to recover the true image of the object. The codedimage being transmitted over a communication channel may have beendistorted by various factors. Source of degradation may have arisenoriginally in creating the image from the object. Feature selectionrefers to the selection of certain attributes of the picture. Suchattributes may be required in the recognition, classification, anddecision in a wider context.

[0006] Digital encoding of video, such as that in digital cinema, is anarea that benefits from improved image compression techniques. Digitalimage compression may be generally classified into two categories:loss-less and lossy methods. A loss-less image is recovered without anyloss of information. A lossy method involves an irrecoverable loss ofsome information, depending upon the compression ratio, the quality ofthe compression algorithm, and the implementation of the algorithm.Generally, lossy compression approaches are considered to obtain thecompression ratios desired for a cost-effective digital cinema approach.To achieve digital cinema quality levels, the compression approachshould provide a visually loss-less level of performance. As such,although there is a mathematical loss of information as a result of thecompression process, the image distortion caused by this loss should beimperceptible to a viewer under normal viewing conditions.

[0007] Existing digital image compression technologies have beendeveloped for other applications, namely for television systems. Suchtechnologies have made design compromises appropriate for the intendedapplication, but do not meet the quality requirements needed for cinemapresentation.

[0008] Digital cinema compression technology should provide the visualquality that a moviegoer has previously experienced. Ideally, the visualquality of digital cinema should attempt to exceed that of ahigh-quality release print film. At the same time, the compressiontechnique should have high coding efficiency to be practical. As definedherein, coding efficiency refers to the bit rate needed for thecompressed image quality to meet a certain qualitative level. Moreover,the system and coding technique should have built-in flexibility toaccommodate different formats and should be cost effective; that is, asmall-sized and efficient decoder or encoder process.

[0009] Many compression techniques available offer significant levels ofcompression, but result in a degradation of the quality of the videosignal. Typically, techniques for transferring compressed informationrequire the compressed information to be transferred at a constant bitrate.

[0010] One compression technique capable of offering significant levelsof compression while preserving the desired level of quality for videosignals utilizes adaptively sized blocks and sub-blocks of encodedDiscrete Cosine Transform (DCT) coefficient data. This technique willhereinafter be referred to as the Adaptive Block Size Discrete CosineTransform (ABSDCT) method. This technique is disclosed in U.S. Pat. No.5,021,891, entitled “Adaptive Block Size Image Compression Method AndSystem,” assigned to the assignee of the present invention andincorporated herein by reference. DCT techniques are also disclosed inU.S. Pat. No. 5,107,345, entitled “Adaptive Block Size Image CompressionMethod And System,” assigned to the assignee of the present inventionand incorporated herein by reference. Further, the use of the ABSDCTtechnique in combination with a Differential Quadtree Transformtechnique is discussed in U.S. Pat. No. 5,452,104, entitled “AdaptiveBlock Size Image Compression Method And System,” also assigned to theassignee of the present invention and incorporated herein by reference.The systems disclosed in these patents utilize what is referred to as“intra-frame” encoding, where each frame of image data is encodedwithout regard to the content of any other frame. Using the ABSDCTtechnique, the achievable data rate may be reduced from around 1.5billion bits per second to approximately 50 million bits per secondwithout discernible degradation of the image quality.

[0011] The ABSDCT technique may be used to compress either a black andwhite or a color image or signal representing the image. The color inputsignal may be in a YIQ format, with Y being the luminance, orbrightness, sample, and I and Q being the chrominance, or color, samplesfor each 4:4:4 or alternate format. Other known formats such as the YUV,YC_(b)C_(r) or RGB formats may also be used. Because of the low spatialsensitivity of the eye to color, most research has shown that asub-sample of the color components by a factor of four in the horizontaland vertical directions is reasonable. Accordingly, a video signal maybe represented by four luminance samples and two chrominance samples.

[0012] Using ABSDCT, a video signal will generally be segmented intoblocks of pixels for processing. For each block, the luminance andchrominance components are passed to a block size assignment element, ora block interleaver. For example, a 16×16 (pixel) block may be presentedto the block interleaver, which orders or organizes the image sampleswithin each 16×16 block to produce blocks and composite sub-blocks ofdata for discrete cosine transform (DCT) analysis. The DCT operator isone method of converting a time and spatial sampled signal to afrequency representation of the same signal. By converting to afrequency representation, the DCT techniques have been shown to allowfor very high levels of compression, as quantizers can be designed totake advantage of the frequency distribution characteristics of animage. In a preferred embodiment, one 16×16 DCT is applied to a firstordering, four 8×8 DCTs are applied to a second ordering, 16 4×4 DCTsare applied to a third ordering, and 64 2×2 DCTs are applied to a fourthordering.

[0013] The DCT operation reduces the spatial redundancy inherent in thevideo source. After the DCT is performed, most of the video signalenergy tends to be concentrated in a few DCT coefficients. An additionaltransform, the Differential Quad-Tree Transform (DQT), may be used toreduce the redundancy among the DCT coefficients.

[0014] For the 16×16 block and each sub-block, the DCT coefficientvalues and the DQT value (if the DQT is used) are analyzed to determinethe number of bits required to encode the block or sub-block. Then, theblock or the combination of sub-blocks that requires the least number ofbits to encode is chosen to represent the image segment. For example,two 8×8 sub-blocks, six 4×4 sub-blocks, and eight 2×2 sub-blocks may bechosen to represent the image segment.

[0015] The chosen block or combination of sub-blocks is then properlyarranged in order into a 16×16 block. The DCT/DQT coefficient values maythen undergo frequency weighting, quantization, and coding (such asvariable length coding) in preparation for transmission. Although theABSDCT technique described above performs remarkably well, it iscomputationally intensive.

[0016] Further, although use of the ABSDCT is visually lossless, it issometimes desirable to recover data in the exact manner in which it wasencoded. For example, mastering and archival purposes require tocompress data in such a way as to be able to recover it exactly in itsnative domain.

[0017] Traditionally, a lossless compression system for images consistsof a predictor, which estimates the value of the current pixel to beencoded. A residual pixel is obtained as the difference between theactual and the predicted pixel. The residual pixel is then entropyencoded and stored or transmitted. Since the prediction removes pixelcorrelation, the residual pixels have a reduced dynamic range with acharacteristic two-sided exponential (Laplacian) distribution. Hence thecompression. The amount of compression of the residuals depends on boththe prediction and subsequent entropy encoding methods. Most commonlyused prediction methods are differential pulse code modulation (DPCM)and its variants such as the adaptive DPCM (ADPCM).

[0018] A problem with pel-based prediction is that the residuals stillhave a high energy. It is due to the fact that only a small number ofneighboring pixels are used in the prediction process. Therefore thereis room to improve the coding efficiency of pel-based prediction schemes

SUMMARY OF THE INVENTION

[0019] Embodiments of the invention describe a system to encode digitalimage and video data in a lossless manner to achieve compression. Thesystem is hybrid—meaning that it has a part that compresses the saiddata in a lossy manner and a part that compresses the residual data in alossless fashion. For the lossy part, the system uses the adaptive blocksize discrete cosine transform (ABSDCT) algorithm. The ABSDCT systemcompresses the said data yielding a high visual quality and compressionratio. A residual image is obtained as the difference between theoriginal and the decompressed one from the ABSDCT system. This residualis encoded losslessly using Golomb-Rice coding algorithm. Due tovisually based adaptive block size and quantization of the DCTcoefficients, the residuals have a very low energy, thus yielding goodoverall lossless compression ratios.

[0020] The ABSDCT system achieves a high compression ratio at cinemaquality. Since it is block-based, it removes pixel correlation muchbetter than any pel-based scheme. Therefore it is used as a predictor inthe lossless system to be described here. In conjunction with thispredictor a lossless encoding system is added to form a hybrid losslesscompression system. It should be noted that the system is capable ofcompressing still images as well as motion images. If it is a stillimage, only the ABSDCT compressed data and entropy encoded residual dataare used as the compressed output. For motion sequences, a decision ismade whether to use intra-frame or inter-frame compression. For example,if ƒ(t) represents an image frame at time instant t, F(t) and F(t+Δt)denote the DCTs of the image frames at time instants t and t+Δt,respectively. Note that Δt corresponds to the time interval between twoconsecutive frames.

[0021] The invention is embodied in an apparatus and method forcompressing data that allows one to be able to recover the data in theexact manner in which the data was encoded. Embodiments comprise asystem that performs intraframe coding, interframe coding, or a hybridof the two. The system is a quality-based system that utilizesadaptively sized blocks and sub-blocks of Discrete Cosine Transformcoefficient data. A block of pixel data is input to an encoder. Theencoder comprises a block size assignment (BSA) element, which segmentsthe input block of pixels for processing. The block size assignment isbased on the variances of the input block and further subdivided blocks.In general, areas with larger variances are subdivided into smallerblocks, and areas with smaller variances are not be subdivided, providedthe block and sub-block mean values fall into different predeterminedranges. Thus, first the variance threshold of a block is modified fromits nominal value depending on its mean value, and then the variance ofthe block is compared with this threshold, and if the variance isgreater than the threshold, then the block is subdivided.

[0022] The block size assignment is provided to a transform element,which transforms the pixel data into frequency domain data. Thetransform is performed only on the block and sub-blocks selected throughblock size assignment. For AC elements, the transform data thenundergoes scaling through quantization and serialization. Quantizationof the transform data is quantized based on an image quality metric,such as a scale factor that adjusts with respect to contrast,coefficient count, rate distortion, density of the block sizeassignments, and/or past scale factors. Serialization, such as zigzagscanning, is based on creating the longest possible run lengths of thesame value. The stream of data is then coded by a variable length coderin preparation for transmission. Coding may be Huffman coding, or codingmay be based on an exponential distribution, such as Golomb-Riceencoding.

[0023] The use of a hybrid compression system such as the ABSDCT, actslike a good predictor of pixel or DCT values. Therefore it results in ahigher lossless compression ratio than the systems using pel-basedprediction. The lossy portion provides digital cinema qualityresults—that is, the compression results in a file that is visuallylossless. For the lossless portion, unlike Huffman codes, Golomb-Ricecoding does not require any a priori code generation. Therefore, it doesnot require an extensive codebook to be stored as in Huffman coding.This results in an efficient use of the chip real estate. Hence, thechip size is reduced in hardware implementation. Further, theGolomb-Rice encoding is much simpler to implement than Huffman coding.Also, Golomb-Rice coding achieves a higher coding efficiency than theHuffman coding as the DCT coefficients or residuals have an exponentialdistribution naturally. Further, as the lossy portion of the compressionsystem uses visually significant information in the block sub-division,context modeling is inherent in the residual encoding. This is importantin that no extra storage registers are needed in gathering contextualdata for the residual encoding. Since no motion estimation is used, thesystem is very simple to implement also.

[0024] An apparatus and method for losslessly compressing and encodingsignals representing image information is claimed. Signals representingimage information are compressed to create a compressed version of theimage. The compressed version of the image is quantized, therebycreating a lossy version of the image. The compressed version of theimage is also serialized to create a serialized quantized compressedversion of the image. This version is then decompressed, and thedifferences between the original image and the decompressed version aredetermined, thereby creating a residual version of the image. The lossyversion of the image and the residual version of the image may be outputseparately or combined, wherein the combination of the decompressedlossy version of the image and the residual version of the image issubstantially the same as the original image.

[0025] A method of losslessly compressing and encoding signalsrepresenting image information is claimed. A lossy compressed data fileand a residual compressed data file are generated. When the lossycompressed data file and the residual compressed data file are combined,a lossless data file that is substantially identical to the originaldata file is created.

[0026] Accordingly, it is an aspect of an embodiment to provide anapparatus and method to efficiently provide lossless compression.

[0027] It is another aspect of an embodiment that compresses digitalimage and audio information losslessly in a manner conducive tomastering and archival purposes.

[0028] It is another aspect of an embodiment to provide a losslesscompression system on an interframe basis.

[0029] It is another aspect of an embodiment to provide a losslesscompression system on an intraframe basis.

BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The features and advantages of the present invention will becomemore apparent from the detailed description set forth below when takenin conjunction with the drawings in which like reference charactersidentify correspondingly throughout and wherein:

[0031]FIG. 1 is a block diagram of an encoder portion of an imagecompression and processing system;

[0032]FIG. 2 is a block diagram of a decoder portion of an imagecompression and processing system;

[0033]FIG. 3 is a flow diagram illustrating the processing stepsinvolved in variance based block size assignment;

[0034]FIG. 4a illustrates an exponential distribution of the Y componentrun-lengths in a DCT coefficient matrix;

[0035]FIG. 4b illustrates an exponential distribution of the C_(b)component run-lengths in a DCT coefficient matrix;

[0036]FIG. 4c illustrates an exponential distribution of the C_(r)component run-lengths in a DCT coefficient matrix;

[0037]FIG. 5a illustrates an exponential distribution of the amplitudesize of the Y component or amplitude size of the Y component in a DCTcoefficient matrix;

[0038]FIG. 5b illustrates an exponential distribution of the amplitudesize of the C_(b) component or amplitude size of the C_(b) component ina DCT coefficient matrix;

[0039]FIG. 5c illustrates an exponential distribution of the amplitudesize of the C_(r) component or amplitude size of the C_(r) component ina DCT coefficient matrix;

[0040]FIG. 6 illustrates a Golomb-Rice encoding process;

[0041]FIG. 7 illustrates an apparatus for Golomb-Rice encoding;

[0042]FIG. 8 illustrates a process of encoding DC component values;

[0043]FIG. 9 illustrates an apparatus for lossless compression; and

[0044]FIG. 10 illustrates a method of hybrid lossless compression.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0045] In order to facilitate digital transmission of digital signalsand enjoy the corresponding benefits, it is generally necessary toemploy some form of signal compression. While achieving high compressionin a resulting image, it is also important that high quality of theimage be maintained. Furthermore, computational efficiency is desiredfor compact hardware implementation, which is important in manyapplications.

[0046] Before one embodiment of the invention is explained in detail, itis to be understood that the invention is not limited in its applicationto the details of the construction and the arrangement of the componentsset forth in the following description or illustrated in the drawings.The invention is capable of other embodiments and are carried out invarious ways. Also, it is understood that the phraseology andterminology used herein is for purpose of description and should not beregarded as limiting.

[0047] Image compression employed in an aspect of an embodiment is basedon discrete cosine transform (DCT) techniques, such as that disclosed inco-pending U.S. patent application “Contrast Sensitive Variance BasedAdaptive Block Size DCT Image Compression”, Ser. No. 09/436,085 filed onNov. 8, 1999, assigned to the assignee of the present application andincorporated herein by reference. Image Compression and Decompressionsystems utilizing the DCT are described in co-pending U.S. patentapplication “Quality Based Image Compression”, Ser. No. 09/494,192,filed on Jan. 28, 2000, assigned to the assignee of the presentapplication and incorporated herein by reference. Generally, an image tobe processed in the digital domain is composed of pixel data dividedinto an array of non-overlapping blocks, N×N in size. A two-dimensionalDCT may be performed on each block. The two-dimensional DCT is definedby the following relationship:${{X\left( {k,l} \right)} = {\frac{{\alpha (k)}{\beta (l)}}{\sqrt{N*M}}{\sum\limits_{m = 0}^{N - 1}{\sum\limits_{n = 0}^{N - 1}{{x\left( {m,n} \right)}{\cos \left\lbrack \frac{\left( {{2m} + 1} \right)\pi \quad k}{2N} \right\rbrack}{\cos \left\lbrack \frac{\left( {{2n} + 1} \right)\pi \quad l}{2N} \right\rbrack}}}}}},{0 \leq k},{l \leq {N - 1}}$${w\quad h\quad e\quad r\quad e\quad {\alpha (k)}},{{\beta (k)} = \left\{ {\begin{matrix}{1,} & {{i\quad f\quad k} = 0} \\{\sqrt{2},} & {{i\quad f\quad k} \neq 0}\end{matrix},} \right.}$

[0048] and

[0049] x(m,n) is the pixel at location (m,n) within an N×M block, and

[0050] X(k,l) is the corresponding DCT coefficient.

[0051] Since pixel values are non-negative, the DCT component X(0,0) isalways positive and usually has the most energy. In fact, for typicalimages, most of the transform energy is concentrated around thecomponent X(0,0). This energy compaction property is what makes the DCTtechnique such an attractive compression method.

[0052] The image compression technique utilizes contrast adaptive codingto achieve further bit rate reduction. It has been observed that mostnatural images are made up of relatively slow varying flat areas, andbusy areas such as object boundaries and high-contrast texture. Contrastadaptive coding schemes take advantage of this factor by assigning morebits to the busy areas and less bits to the less busy areas.

[0053] Contrast adaptive methods utilize intraframe coding (spatialprocessing) instead of interframe coding (spatio-temporal processing).Interframe coding inherently requires multiple frame buffers in additionto more complex processing circuits. In many applications, reducedcomplexity is needed for actual implementation. Intraframe coding isalso useful in a situation that can make a spatio-temporal coding schemebreak down and perform poorly. For example, 24 frame per second moviescan fall into this category since the integration time, due to themechanical shutter, is relatively short. The short integration timeallows a higher degree of temporal aliasing. The assumption offrame-to-frame correlation breaks down for rapid motion as it becomesjerky. Intraframe coding is also easier to standardize when both 50 Hzand 60 Hz power line frequencies are involved. Television currentlytransmits signals at either 50 Hz or 60 Hz. The use of an intraframescheme, being a digital approach, can adapt to both 50 Hz and 60 Hzoperation, or even to 24 frame per second movies by trading off framerate versus spatial resolution.

[0054] For image processing purposes, the DCT operation is performed onpixel data that is divided into an array of non-overlapping blocks. Notethat although block sizes are discussed herein as being N×N in size, itis envisioned that various block sizes may be used. For example, a N×Mblock size may be utilized where both N and M are integers with M beingeither greater than or less than N. Another important aspect is that theblock is divisible into at least one level of sub-blocks, such asN/i×N/i, N/i×N/j, N/i×M/j, and etc. where i and j are integers.Furthermore, the exemplary block size as discussed herein is a 16×16pixel block with corresponding block and sub-blocks of DCT coefficients.It is further envisioned that various other integers such as both evenor odd integer values may be used, e.g. 9×9.

[0055]FIGS. 1 and 2 illustrate an image processing system 100incorporating the concept of configurable serializer. The imageprocessing system 100 comprises an encoder 104 that compresses areceived video signal. The compressed signal is transmitted using atransmission channel or a physical medium 108, and received by a decoder112. The decoder 112 decodes the received encoded data into imagesamples, which may then be exhibited.

[0056] In general, an image is divided into blocks of pixels forprocessing. A color signal may be converted from RGB space to YC₁C₂space using a RGB to YC₁C₂ converter 116, where Y is the luminance, orbrightness, component, and C₁ and C₂ are the chrominance, or color,components. Because of the low spatial sensitivity of the eye to color,many systems sub-sample the C₁ and C₂ components by a factor of four inthe horizontal and vertical directions. However, the sub-sampling is notnecessary. A full resolution image, known as 4:4:4 format, may be eithervery useful or necessary in some applications such as those referred toas covering “digital cinema.” Two possible YC₁C₂ representations are,the YIQ representation and the YUV representation, both of which arewell known in the art. It is also possible to employ a variation of theYUV representation known as YCbCr. This may be further broken into oddand even components. Accordingly, in an embodiment the representationY-even, Y-odd, Cb-even, Cb-odd, Cr-even, Cr-odd is used.

[0057] In a preferred embodiment, each of the even and odd Y, Cb, and Crcomponents is processed without sub-sampling. Thus, an input of each ofthe six components of a 16×16 block of pixels is provided to the encoder104. For illustration purposes, the encoder 104 for the Y-even componentis illustrated. Similar encoders are used for the Y-odd component, andeven and odd Cb and Cr components. The encoder 104 comprises a blocksize assignment element 120, which performs block size assignment inpreparation for video compression. The block size assignment element 120determines the block decomposition of the 16×16 block based on theperceptual characteristics of the image in the block. Block sizeassignment subdivides each 16×16 block into smaller blocks, such as 8×8,4×4, and 2×2, in a quad-tree fashion depending on the activity within a16×16 block. The block size assignment element 120 generates a quad-treedata, called the PQR data, whose length can be between 1 and 21 bits.Thus, if block size assignment determines that a 16×16 block is to bedivided, the R bit of the PQR data is set and is followed by fouradditional bits of Q data corresponding to the four divided 8×8 blocks.If block size assignment determines that any of the 8×8 blocks is to besubdivided, then four additional bits of P data for each 8×8 blocksubdivided are added.

[0058] Referring now to FIG. 3, a flow diagram showing details of theoperation of the block size assignment element 120 is provided. Thevariance of a block is used as a metric in the decision to subdivide ablock. Beginning at step 202, a 16×16 block of pixels is read. At step204, the variance, v16, of the 16×16 block is computed. The variance iscomputed as follows:${var} = {{\frac{1}{N^{2}}{\sum\limits_{i = 0}^{N - 1}{\sum\limits_{j = 0}^{N - 1}x_{i,j}^{2}}}} - \left( {\frac{1}{N^{2}}{\sum\limits_{i = 0}^{N - 1}{\sum\limits_{j = 0}^{N - 1}x_{i,j}}}} \right)^{2}}$

[0059] where N=16, and x_(ij) is the pixel in the i^(th) row, j^(th)column within the N×N block. At step 206, first the variance thresholdT16 is modified to provide a new threshold T'16 if the mean value of theblock is between two predetermined values, then the block variance iscompared against the new threshold, T'16.

[0060] If the variance v16 is not greater than the threshold T16, thenat step 208, the starting address of the 16×16 block is written intotemporary storage, and the R bit of the PQR data is set to 0 to indicatethat the 16×16 block is not subdivided. The algorithm then reads thenext 16×16 block of pixels. If the variance v16 is greater than thethreshold T16, then at step 210, the R bit of the PQR data is set to 1to indicate that the 16×16 block is to be subdivided into four 8×8blocks.

[0061] The four 8×8 blocks, i=1:4, are considered sequentially forfurther subdivision, as shown in step 212. For each 8×8 block, thevariance, v8 _(i), is computed, at step 214. At step 216, first thevariance threshold T8 is modified to provide a new threshold T'8 if themean value of the block is between two predetermined values, then theblock variance is compared to this new threshold.

[0062] If the variance v8 _(i) is not greater than the threshold T8,then at step 218, the starting address of the 8×8 block is written intotemporary storage, and the corresponding Q bit, Q_(i), is set to 0. Thenext 8×8 block is then processed. If the variance v8 _(i) is greaterthan the threshold T8, then at step 220, the corresponding Q bit, Q_(i),is set to 1 to indicate that the 8×8 block is to be subdivided into four4×4 blocks.

[0063] The four 4×4 blocks, j_(i)=1:4, are considered sequentially forfurther subdivision, as shown in step 222. For each 4×4 block, thevariance, v4 _(ij), is computed, at step 224. At step 226, first thevariance threshold T4 is modified to provide a new threshold T'4 if themean value of the block is between two predetermined values, then theblock variance is compared to this new threshold.

[0064] If the variance v4 _(ij) is not greater than the threshold T4,then at step 228, the address of the 4×4 block is written, and thecorresponding P bit, P_(ij) is set to 0. The next 4×4 block is thenprocessed. If the variance v4 _(ij) is greater than the threshold T4,then at step 230, the corresponding P bit, P_(ij), is set to 1 toindicate that the 4×4 block is to be subdivided into four 2×2 blocks. Inaddition, the address of the 4 2×2 blocks are written into temporarystorage.

[0065] The thresholds T16, T8, and T4 may be predetermined constants.This is known as the hard decision. Alternatively, an adaptive or softdecision may be implemented. For example, the soft decision varies thethresholds for the variances depending on the mean pixel value of the2N×2N blocks, where N can be 8, 4, or 2. Thus, functions of the meanpixel values, may be used as the thresholds.

[0066] For purposes of illustration, consider the following example. Letthe predetermined variance thresholds for the Y component be 50, 1100,and 880 for the 16×16, 8×8, and 4×4 blocks, respectively. In otherwords, T16=50, T8=1100, and T4=880. Let the range of mean values be 80and 100. Suppose the computed variance for the 16×16 block is 60. Since60 is greater than T16, and the mean value 90 is between 80 and 100, the16×16 block is subdivided into four 8×8 sub-blocks. Suppose the computedvariances for the 8×8 blocks are 1180, 935, 980, and 1210. Since two ofthe 8×8 blocks have variances that exceed T8, these two blocks arefurther subdivided to produce a total of eight 4×4 sub-blocks. Finally,suppose the variances of the eight 4×4 blocks are 620, 630, 670, 610,590, 525, 930, and 690, with corresponding means values 90, 120, 110,115. Since the mean value of the first 4×4 block falls in the range (80,100), its threshold will be lowered to T'4=200 which is less than 880.So, this 4×4 block will be subdivided as well as the seventh 4×4 block.

[0067] Note that a similar procedure is used to assign block sizes forthe luminance component Y-odd and the color components, C_(b) and C_(r).The color components may be decimated horizontally, vertically, or both.

[0068] Additionally, note that although block size assignment has beendescribed as a top down approach, in which the largest block (16×16 inthe present example) is evaluated first, a bottom up approach mayinstead be used. The bottom up approach will evaluate the smallestblocks (2×2 in the present example) first.

[0069] Referring back to FIG. 1, the PQR data, along with the addressesof the selected blocks, are provided to a DCT element 124. The DCTelement 124 uses the PQR data to perform discrete cosine transforms ofthe appropriate sizes on the selected blocks. Only the selected blocksneed to undergo DCT processing.

[0070] The image processing system 100 also comprises DQT element 128for reducing the redundancy among the DC coefficients of the DCTs. A DCcoefficient is encountered at the top left corner of each DCT block. TheDC coefficients are, in general, large compared to the AC coefficients.The discrepancy in sizes makes it difficult to design an efficientvariable length coder. Accordingly, it is advantageous to reduce theredundancy among the DC coefficients.

[0071] The DQT element 128 performs 2-D DCTs on the DC coefficients,taken 2×2 at a time. Starting with 2×2 blocks within 4×4 blocks, a 2-DDCT is performed on the four DC coefficients. This 2×2 DCT is called thedifferential quad-tree transform, or DQT, of the four DC coefficients.Next, the DC coefficient of the DQT along with the three neighboring DCcoefficients within an 8×8 block are used to compute the next level DQT.Finally, the DC coefficients of the four 8×8 blocks within a 16×16 blockare used to compute the DQT. Thus, in a 16×16 block, there is one trueDC coefficient and the rest are AC coefficients corresponding to the DCTand DQT.

[0072] The transform coefficients (both DCT and DQT) are provided to aquantizer for quantization. In a preferred embodiment, the DCTcoefficients are quantized using frequency weighting masks (FWMs) and aquantization scale factor. A FWM is a table of frequency weights of thesame dimensions as the block of input DCT coefficients. The frequencyweights apply different weights to the different DCT coefficients. Theweights are designed to emphasize the input samples having frequencycontent that the human visual or optical system is more sensitive to,and to de-emphasize samples having frequency content that the visual oroptical system is less sensitive to. The weights may also be designedbased on factors such as viewing distances, etc.

[0073] The weights are selected based on empirical data. A method fordesigning the weighting masks for 8×8 DCT coefficients is disclosed inISO/IEC JTC1 CD 10918, “Digital compression and encoding ofcontinuous-tone still images—part 1: Requirements and guidelines,”International Standards Organization, 1994, which is incorporated hereinby reference. In general, two FWMs are designed, one for the luminancecomponent and one for the chrominance components. The FWM tables forblock sizes 2×2, 4×4 are obtained by decimation and 16×16 byinterpolation of that for the 8×8 block. The scale factor controls thequality and bit rate of the quantized coefficients.

[0074] Thus, each DCT coefficient is quantized according to therelationship:${D\quad C\quad {T_{q}\left( {i,j} \right)}} = \left\lfloor {\frac{8*D\quad C\quad {T\left( {i,j} \right)}}{{{fwm}\left( {i,j} \right)}*q} \pm \frac{1}{2}} \right\rfloor$

[0075] where DCT(i,j) is the input DCT coefficient, fwm(i,j) is thefrequency-weighting mask, q is the scale factor, and DCTq(i,j) is thequantized coefficient. Note that depending on the sign of the DCTcoefficient, the first term inside the braces is rounded up or down. TheDQT coefficients are also quantized using a suitable weighting mask.However, multiple tables or masks can be used, and applied to each ofthe Y, Cb, and Cr components.

[0076] AC values are then separated 130 from DC values and processedseparately. For DC elements, a first DC component value of each slice isencoded. Each subsequent DC component value of each slice is thenrepresented as the difference between it and the DC component valuepreceding it, and encoded 134. For lossless encoding, the initial DCcomponent value of each slice and the differences are encoded 138 usingGolomb-Rice, as described with respect to FIGS. 6 and 8. Use ofGolomb-Rice encoding for the differences between successive DC componentvalues is advantageous in that the differentials of the DC componentvalues tend to have a two-sided exponential distribution. The data maythen be temporarily stored using a buffer 142, and then transferred ortransmitted to the decoder 112 through the transmission channel 108.

[0077]FIG. 8 illustrates a process of encoding DC component values. Theprocess is equally applicable for still image, video image (such as, butnot limited to, motion pictures or high-definition television) andaudio. For a given slice of data 804, a first DC component value of theslice is retrieved 808. The first DC component value is then coded 812.Unlike AC component values, the DC component values need not bequantized. In an embodiment, a single DC value for a 16×16 block is usedregardless of the block size assignment breakdown. It is contemplatedthat any fixed sized block, such as 8×8 or 4×4, or any variable blocksize as defined by the block size assignment, may be used. The second,or next, DC component value of a given slice is then retrieved 816. Thesecond DC component value is then compared with the first DC componentvalue, and the difference, or residual, is encoded 820. Thus, the secondDC component value need only be represented as the difference between itand the first value. This process is repeated for each DC componentvalue of a slice. Thus, an inquiry 824 is made as to whether the end ofthe slice (last block and therefore last DC value) is reached. If not828, the next DC value of the slice is retrieved 816, and the processrepeats. If so 832, the next slice is retrieved 804, and the processrepeats until all of the slices of a frame, and all of the frames of thefile are processed.

[0078] An objective of lossless encoding of DC component values is togenerate residual values that tend to have a low variance. In usingDCTs, the DC coefficient component value contributes the maximum pixelenergy. Therefore, by not quantizing the DC component values, thevariance of the residuals is reduced.

[0079] For AC elements, the block of data and frequency weighting masksare then scaled by a quantizer 146, or a scale factor element.Quantization of the DCT coefficients reduces a large number of them tozero which results in compression. In a preferred embodiment, there are32 scale factors corresponding to average bit rates. Unlike othercompression methods such as MPEG2, the average bit rate is controlledbased on the quality of the processed image, instead of target bit rateand buffer status.

[0080] To increase compression further, the quantized coefficients areprovided to a scan serializer 150. The serializer 150 scans the blocksof quantized coefficients to produce a serialized stream of quantizedcoefficients. Zigzag scans, column scanning, or row scanning may beemployed. A number of different zigzag scanning patterns, as well aspatterns other than zigzag may also be chosen. A preferred techniqueemploys 8×8 block sizes for the zigzag scanning. A zigzag scanning ofthe quantized coefficients improves the chances of encountering a largerun of zero values. This zero run inherently has a decreasingprobability, and may be efficiently encoded using Huffman codes.

[0081] The stream of serialized, quantized AC coefficients is providedto a variable length coder 154. The AC component values may be encodedeither using Huffman encoding or Golomb-Rice encoding. For DC componentvalues, Golomb-Rice encoding is utilized. A run-length coder separatesthe coefficients between the zero from the non-zero coefficients, and isdescribed in detail with respect to FIG. 6. In an embodiment,Golomb-Rice coding is utilized. Golomb-Rice encoding is efficient incoding non-negative integers with an exponential distribution. UsingGolomb codes is more optimal for compression in providing shorter lengthcodes for exponentially distributed variables.

[0082] In Golomb encoding run-lengths, Golomb codes are parameterized bya non-negative integer m. For example, given a parameter m, the Golombcoding of a positive integer n is represented by the quotient of n/m inunary code followed by the remainder represented by a modified binarycode, which is └log₂m┘ bits long if the remainder is less than or equalto 2^(┌log 2m)┐−m, otherwise, ┌log₂ m┐ bits long. Golomb-Rice coding isa special case of Golomb coding where the parameter m is expressed asm=2^(k). In such a case the quotient of n/m is obtained by shifting thebinary representation of the integer n to the right by k bits, and theremainder of n/m is expressed by the least k bits of n. Thus, theGolomb-Rice code is the concatenation of the two. Golomb-Rice coding canbe used to encode both positive and negative integers with a two-sidedgeometric (exponential) distribution as given by

p _(α)(x)=cα^(|x|)  (1)

[0083] In (1), α is a parameter that characterizes the decay of theprobability of x, and c is a normalization constant. Since p_(α)(x) ismonotonic, it can be seen that a sequence of integer values shouldsatisfy

p ₆₀ (x _(i)=0)≧p _(α)(x _(i)=−1)≧p _(α)(x _(i)=+1) ≧p _(α)(x_(i)=−2)≧  (2)

[0084] As illustrated in FIGS. 4a, 4 b, 4 c and 5 a, 5 b, 5 c, both thezero-runs and amplitudes in a quantized DCT coefficient matrix haveexponential distributions. The distributions illustrated in thesefigures are based on data from real images. FIG. 4a illustrates the Ycomponent distribution 400 of zero run-lengths versus relativefrequency. Similarly, FIGS. 4b and 4 c illustrates the Cb and Crcomponent distribution, of zero run-lengths versus relative frequency410 and 420, respectively. FIG. 5a illustrates the Y componentdistribution 500 of amplitude size versus relative frequency. Similarly,FIGS. 5b and 5 c illustrates the Cb and Cr component distribution ofamplitude size versus relative frequency, 510 and 520, respectively.Note that in FIGS. 5a, 5 b, and 5 c the plots represent the distributionof the size of the DCT coefficients. Each size represents a range ofcoefficient values. For example, a size value of four has the range{−15,−14, . . . ,−8,8, . . . ,14,15}, a total of 16 values. Similarly, asize value of ten has the range {−1023,−1022, . . . ,−512,512, . . .,1022,1023}, a total of 1024 values. It is seen from FIGS. 4a, 4 b, 4 c,5 a, 5 b and 5 c that both run-lengths and amplitude size haveexponential distributions. The actual distribution of the amplitudes canbe shown to fit the following equation (3): $\begin{matrix}{{{p\left( X_{k,l} \right)} = {\frac{\sqrt{2\lambda}}{2}\exp \left\{ \left. {- \sqrt{2\lambda}} \middle| X_{k,l} \right| \right\} k}},{l \neq 0}} & (3)\end{matrix}$

[0085] In (3), X_(k,l) represents the DCT coefficient corresponding tofrequency k and l in the vertical and horizontal dimensions,respectively, and the mean ${\mu_{x} = \frac{1}{\sqrt{2\lambda}}},$

[0086] variance $\sigma_{x}^{2} = {\frac{1}{2\lambda}.}$

[0087] Accordingly, the use of Golomb-Rice coding in the mannerdescribed is more optimal in processing data in DCTs.

[0088] Although the following is described with respect to compressionof image data, the embodiments are equally applicable to embodimentscompressing audio data. In compressing image data, the image or videosignal may be, for example, either in RGB, or YIQ, or YUV, or Y Cb Crcomponents with linear or log encoded pixel values.

[0089]FIG. 6 illustrates the process 600 of encoding zero and non-zerocoefficients. As the DCT matrix is scanned, the zero and non-zerocoefficients are processed separately and separated 604. For zero data,the length of zero run is determined 608. Note that run-lengths arepositive integers. For example, if the run-length is found to be n, thena Golomb parameter m is determined 612. In an embodiment, the Golombparameter is determined as a function of the run length. In anotherembodiment, the Golomb parameter (m) is determined by the followingequation (4)

m=┌log₂ n┐  (4)

[0090] Optionally, the length of run-lengths and associated Golombparameters are counted 616 by a counter or register. To encode the runlength of zeros n, a quotient is encoded 620. In an embodiment, thequotient is determined as a function of the run length of zeros and theGolomb parameter. In another embodiment, the quotient (Q) is determinedby the following equation (5):

Q=└n/2^(m)┘  (5)

[0091] In an embodiment, the quotient Q is encoded in unary code, whichrequires Q+1 bits. Next, a remainder is encoded 624. In an embodiment,the remainder is encoded as a function of the run length and thequotient. In another embodiment, the remainder (R) is determined usingthe following equation (6):

R=n−2^(m)Q   (6)

[0092] In an embodiment, the remainder R is encoded in an m-bit binarycode. After, the quotient Q and the remainder R are determined, thecodes for Q and R are concatenated 628 to represent an overall code forthe run length of zeros n.

[0093] Nonzero coefficients are also encoded using Golomb-Rice. Sincethe coefficient amplitude can be positive or negative, it is necessaryto use a sign bit and to encode the absolute value of a given amplitude.Given the amplitude of the non-zero coefficient being x, the amplitudemay be expressed as a function of the absolute value of the amplitudeand the sign. Accordingly, the amplitude may be expressed as y using thefollowing equation (7): $\begin{matrix}{y = \left\{ \begin{matrix}{{2x},} & {{i\quad f\quad x} \geq 0} \\{\left. 2 \middle| x \middle| {- 1} \right.,} & {o\quad t\quad h\quad e\quad r\quad w\quad i\quad s\quad e}\end{matrix} \right.} & (7)\end{matrix}$

[0094] Accordingly, the value of a non-zero coefficient is optionallycounted by a counter, or register, 632. It is then determined 636 if theamplitude is greater than or equal to zero. If it is, the value isencoded 640 as twice the given value. If not, the value is encoded 644as one less than twice the absolute value. It is contemplated that othermapping schemes may also be employed. The key is that an extra bit todistinguish the sign of the value is not needed.

[0095] Encoding amplitudes as expressed by equation (7) results in thatpositive values of x being even integers and negative values become oddintegers. Further, this mapping preserves the probability assignment ofx as in (2). An advantage of encoding as illustrated in equation (7)allows one to avoid using a sign bit to represent positive and negativenumbers. After the mapping is done, y is encoded in the same manner aswas done for the zero-run. The procedure is continued until allcoefficients have been scanned in the current block.

[0096] It is important to recognize that although embodiments of theinvention are determine values of coefficients and run lengths as afunction of equations (1)-(7), the exact equations (1)-(7) need not beused. It is the exploitation of the exponential distribution ofGolomb-Rice encoding and of DCT coefficients that allows for moreefficient compression of image and audio data.

[0097] Since a zero-run after encoding is not distinguishable from anon-zero amplitude, it may be necessary to use a special prefix code offixed length to mark the occurrence of the first zero-run. It is commonto encounter all zeros in a block after a non-zero amplitude has beenencountered. In such cases, it may be more efficient to use a codereferring to end-of-block (EOB) code rather than Golomb-Rice code. TheEOB code is again, optionally, a special fixed length code.

[0098] According to equation (1) or (3), the probability distribution ofthe amplitude or run-length in the DCT coefficient matrix isparameterized by α or λ. The implication is that the coding efficiencymay be improved if the context under which a particular DCT coefficientblock arises. An appropriate Golomb-Rice parameter to encode thequantity of interest may then be used. In an embodiment, counters orregisters are used for each run-length and amplitude size value tocompute the respective cumulative values and the corresponding number oftimes that such a value occurs. For example, if the register to storethe cumulative value and number of elements accumulated are R_(rl), andN_(rl), respectively, the following equation (6) may be used as theRice-Golomb parameter to encode the run-length: $\begin{matrix}\left\lceil {\log_{2}\frac{R_{r\quad l}}{N_{r\quad l}}} \right\rceil & (6)\end{matrix}$

[0099] A similar procedure may be used for the amplitude.

[0100] The residual pixels are generated by first decompressing thecompressed data using the ABSDCT decoder, and then subtracting it fromthe original data. Smaller the residual dynamic range, higher is thecompression. Since the compression is block-based, the residuals arealso generated on a block basis. It is a well-known fact that theresidual pixels have a two-sided exponential distribution, usuallycentered at zero. Since Golomb-Rice codes are more optimal for suchdata, a Golomb-Rice coding procedure is used to compress the residualdata. However, no special codes are necessary, as there are norun-lengths to be encoded. Further, there is no need for an EOB code.Thus, the compressed data consists of two components. One is thecomponent from the lossy compressor and the other is from the losslesscompressor.

[0101] When encoding motion sequences one can benefit from exploitingthe temporal correlation as well. In order to exploit fully the temporalcorrelation, pixel displacement is first estimated due to motion, andthen a motion compensated prediction is performed to obtain residualpixels. As ABSDCT performs adaptive block size encoding, block sizeinformation may be alternatively used as a measure of displacement dueto motion. As a further simplification, no scene change detection isused. Instead, for each frame in a sequence first the intra-framecompressed data is obtained. Then the difference between the current andprevious frame DCTs, are generated on a block-by-block basis. This isdescribed further by U.S. patent application Ser. No. 09/877,578, filedJun. 7, 2001, which is incorporated by reference herein. These residualsin the DCT domain are encoded using both Huffman and Golomb-Rice codingprocedures. The final compressed output then corresponds to the one thatuses the minimum number of bits per frame.

[0102] The lossless compression algorithm is a hybrid scheme that lendsitself well to repurposing and transcoding by stripping off the losslessportion. Thus, using ABSDCT maximizes pixel correlation in the spatialdomain resulting in residual pixels having a lower variance than thoseused in prediction schemes. The lossy portion of the overall systempermits the user to achieve the necessary quality and data rates fordistribution purposes without having to resort to interframe processing,thereby eliminating related motion artifacts and significantly reducingimplementation complexities. This is especially significant in programsbeing distributed for digital cinema applications, since the lossyportion of the compressed material requires a higher level of quality inits distribution.

[0103]FIG. 9 illustrates a hybrid lossless encoding apparatus 900. FIG.10 illustrates a process that may be run on such an apparatus. Originaldigital information 904 resides on a storage device, or is transmitted.Many of the elements in FIG. 9 are described in more detail with respectto FIGS. 1 and 2. Frames of data are sent to a compressor 908,comprising a block size assignment element 912, a DCT/DQT transformelement 916, and a quantizer 920. After the DCT/DQT is performed on thedata, the data is converted into the frequency domain. In one output922, the data is quantized by the quantizer 920 and transferred to anoutput 924, which may comprises storage and/or switching. All of theabove described processing is on an intraframe basis.

[0104] The quantizer output is also transferred to a decompressor 928.The decompressor 928 undoes the process of the compressor, going throughan inverse quantizer 932, and an IDQT/IDCT 936, along with knowledge ofthe PQR data as defined by the BSA. The result of the decompressor 940is fed to a subtractor 944 where it is compared with the original.Subtractor 944 may be a variety of elements, such as a differencer, thatcomputes residual pixels as the difference between the uncompressed andthe compressed and decompressed pixels for each block. Additionally, thedifferencer may obtain the residuals in the DCT domain for each blockfor conditional interframe coding. The result 948 of the comparisonbetween the decompressed data and the original is the pixel residualfile. That is, the result 948 is indicative of the losses experienced bythe data being compressed and uncompressed. Thus, the original data isequal to the output 922 in combination with the result 948. The result948 is then serialized 952 and Huffman and/or Golomb Rice encoder 956,and provided as a second output 960. The Huffman and/or Golomb Riceencoder 956 may be a type of entropy encoder that encodes residualspixels using Golomb Rice coding. A decision is made whether to useintraframe or interframe based on the minimum bits for each frame. Useof Golomb Rice coding of the residuals leads to higher overallcompression ratios of the system.

[0105] Thus, the lossless, interframe output is a combination, or hybridof two sets of data, the lossy, high quality image file (922, or A) andthe residual file (960 or C).

[0106] Interframe coding may also be utilized. The output of thequantizer is transferred to a store 964, along with knowledge of theBSA. Upon gathering of a frame's worth of data, a subtractor 966compares the stored frame 964 with a next frame 968. The differenceresults in a DCT residual 970, which is then serialized and/orGolomb-Rice encoded 974, providing a third output data set 976 to theoutput 924. Thus, an interframe lossless file of B and C is compiled.

[0107] Thus, either combination (A+C or B+C) may be chosen based on sizeconsiderations. Further, a purely intraframe output may be desirable forediting purposes.

[0108] Referring back to FIG. 1, the compressed image signal generatedby the encoder 104 may be temporarily stored using a buffer 142, andthen transmitted to the decoder 112 using the transmission channel 108.The transmission channel 108 may be a physical medium, such as amagnetic or optical storage device, or a wire-line or wirelessconveyance process or apparatus. The PQR data, which contains the blocksize assignment information, is also provided to the decoder 112 (FIG.2). The decoder 112 comprises a buffer 164 and a variable length decoder168, which decodes the run-length values and the non-zero values. Thevariable length decoder 168 operates in a similar but opposite manner asthat described in FIG. 6.

[0109] The output of the variable length decoder 168 is provided to aninverse serializer 172 that orders the coefficients according to thescan scheme employed. For example, if a mixture of zigzag scanning,vertical scanning, and horizontal scanning were used, the inverseserializer 172 would appropriately re-order the coefficients with theknowledge of the type of scanning employed. The inverse serializer 172receives the PQR data to assist in proper ordering of the coefficientsinto a composite coefficient block.

[0110] The composite block is provided to an inverse quantizer 174, forundoing the processing due to the use of the quantizer scale factor andthe frequency weighting masks.

[0111] The coefficient block is then provided to an IDQT element 186,followed by an IDCT element 190, if the Differential Quad-tree transformhad been applied. Otherwise, the coefficient block is provided directlyto the IDCT element 190. The IDQT element 186 and the IDCT element 190inverse transform the coefficients to produce a block of pixel data. Thepixel data may then have to be interpolated, converted to RGB form, andthen stored for future display.

[0112]FIG. 7 illustrates an apparatus for Golomb-Rice encoding 700. Theapparatus in FIG. 7 preferably implements a process as described withrespect to FIG. 6. A determiner 704 determines a run length (n) and aGolomb parameter (m). Optionally, a counter or register 708 is used foreach run-length and amplitude size value to compute the respectivecumulative values and the corresponding number of times that such avalue occurs. An encoder 712 encodes a quotient (Q) as a function of therun length and the Golomb parameter. The encoder 712 also encodes theremainder (R) as a function of the run length, Golomb parameter, andquotient. In an alternate embodiment, encoder 712 also encodes nonzerodata as a function of the non-zero data value and the sign of thenon-zero data value. A concatenator 716 is used to concatenate the Qvalue with the R value.

[0113] As examples, the various illustrative logical blocks, flowcharts,and steps described in connection with the embodiments disclosed hereinmay be implemented or performed in hardware or software with anapplication-specific integrated circuit (ASIC), a programmable logicdevice, discrete gate or transistor logic, discrete hardware components,such as, e.g., registers and FIFO, a processor executing a set offirmware instructions, any conventional programmable software and aprocessor, or any combination thereof. The processor may advantageouslybe a microprocessor, but in the alternative, the processor may be anyconventional processor, controller, microcontroller, or state machine.The software could reside in RAM memory, flash memory, ROM memory,registers, hard disk, a removable disk, a CD-ROM, a DVD-ROM or any otherform of storage medium known in the art.

[0114] The previous description of the preferred embodiments is providedto enable any person skilled in the art to make or use the presentinvention. The various modifications to these embodiments will bereadily apparent to those skilled in the art, and the generic principlesdefined herein may be applied to other embodiments without the use ofthe inventive faculty. Thus, the present invention is not intended to belimited to the embodiments shown herein but is to be accorded the widestscope consistent with the principles and novel features disclosedherein.

[0115] Other features and advantages of the invention are set forth inthe following claims.

1. A method of losslessly compressing and encoding signals representingan image, the method comprising: generating a lossy compressed datafile; generating a residual compressed data file; and combining thelossy data file with the residual data file to create a lossless datafile, wherein the lossless data file is substantially identical to theoriginal data file.
 2. The method as set forth in claim 1, wherein thelossy compressed data file and the residual compressed data file aregenerated on an intraframe basis.
 3. The method as set forth in claim 1,wherein the lossy compressed data file and the residual compressed datafile are generated on an interframe basis.
 4. The method set forth inclaim 1, wherein generating utilizes a combination of discrete cosinetransform (DCT) and discrete quadtree transform (DQT) techniques.
 5. Themethod set forth in claim 1, wherein generating utilizes Golomb-Ricecoding techniques.
 6. An apparatus to losslessly compress and encodesignals representing an image, the apparatus comprising: means forgenerating a lossy compressed data file; means for generating a residualcompressed data file; and means for combining the lossy data file withthe residual data file to create a lossless data file, wherein thelossless data file is substantially identical to the original data file.7. The apparatus as set forth in claim 6, wherein the means forgenerating the lossy compressed data file and the means for generatingthe residual compressed data file are generated on an intraframe basis.8. The apparatus as set forth in claim 6, wherein the means forgenerating the lossy compressed data file and the means for generatingthe residual compressed data file are generated on an interframe basis.9. The apparatus set forth in claim 6, wherein the means for generatingutilizes a combination of discrete cosine transform (DCT) and discretequadtree transform (DQT) techniques.
 10. The method set forth in claim6, wherein the means for generating utilizes Golomb-Rice codingtechniques.
 11. A method for losslessly compressing and encoding signalsrepresenting an image, the method comprising: compressing the signalsrepresenting the image thereby creating a compressed version of theimage; quantizing the compressed version of the image thereby creating alossy version of the image; serializing the quantized compressed versionof the image thereby creating a serialized quantized compressed versionof the image; decompressing the compressed version of the image;determining the differences between the image and the decompressedversion of the image thereby creating a residual version of the image;and outputting the lossy version of the image and the residual versionof the image, wherein the combination of the lossy version of the imageand the residual version of the image is substantially the same as theoriginal image.
 12. The method set forth in claim 11, wherein thelossless compression is on an intraframe basis.
 13. The method set forthin claim 11, wherein compressing utilizes a combination of discretecosine transform (DCT) and discrete quadtree transform (DQT) techniques.14. The method set forth in claim 11, wherein serializing utilizesGolomb-Rice coding techniques.
 15. A method for losslessly compressingand encoding signals representing image information, the imagecomprising a plurality of frames, the method comprising: compressing afirst frame thereby creating a compressed version of the image;quantizing the compressed version of the image thereby creating a lossyversion of the image; serializing the quantized compressed version ofthe image thereby creating a serialized quantized compressed version ofthe image; compressing a second frame of signals representing the image;determining the differences between the first frame and the second frameof the image thereby creating a residual version of the image; andoutputting the lossy version of the image with the residual version ofthe image, wherein the combination of the lossy version of the image andthe residual version of the image is substantially the same as theoriginal image.
 16. The method set forth in claim 15, wherein thelossless compression is on an interframe basis.
 17. The method set forthin claim 15, wherein compressing utilizes a combination of discretecosine transform (DCT) and discrete quadtree transform (DQT) techniques.18. The method set forth in claim 15, wherein serializing utilizesGolomb-Rice coding techniques.
 19. An apparatus for losslesslycompressing and encoding signals representing an image, the methodcomprising: means for compressing the signals representing the imagethereby creating a compressed version of the image; means for quantizingthe compressed version of the image thereby creating a lossy version ofthe image; means for serializing the quantized compressed version of theimage thereby creating a serialized quantized compressed version of theimage; means for decompressing the compressed version of the image;means for determining the differences between the image and thedecompressed version of the image thereby creating a residual version ofthe image; and means for outputting the lossy version of the image andthe residual version of the image, wherein the combination of the lossyversion of the image and the residual version of the image issubstantially the same as the original image.
 20. The apparatus setforth in claim 19, wherein the lossless compression is on an intraframebasis.
 21. The apparatus set forth in claim 19, wherein the means forcompressing utilizes a combination of discrete cosine transform (DCT)and discrete quadtree transform (DQT) techniques.
 22. An apparatus forlosslessly compressing and encoding signals representing imageinformation, the image comprising a plurality of frames, the methodcomprising: means for compressing a first frame thereby creating acompressed version of the image; means for quantizing the compressedversion of the image thereby creating a lossy version of the image;means for serializing the quantized compressed version of the imagethereby creating a serialized quantized compressed version of the image;means for compressing a second frame of signals representing the image;means for determining the differences between the first frame and thesecond frame of the image thereby creating a residual version of theimage; and means for outputting the lossy version of the image with theresidual version of the image, wherein the combination of the lossyversion of the image and the residual version of the image issubstantially the same as the original image.
 23. The apparatus setforth in claim 22, wherein the lossless compression is on an interframebasis.
 24. The apparatus set forth in claim 22, wherein the means forcompressing utilizes a combination of discrete cosine transform (DCT)and discrete quadtree transform (DQT) techniques.
 25. An apparatus forlosslessly compressing and encoding signals representing an image, themethod comprising: a compressor element configured to perform discretecosine transforms (DCTs) and discrete quadtree transforms (DQTs) to thesignals representing the image thereby creating a compressed version ofthe image; a quantizer element coupled to the compressor elementconfigured to quantize the compressed version of the image therebycreating a lossy version of the image; a serializer element coupled tothe quantizer element configured to serialize the quantized compressedversion of the image thereby creating a serialized quantized compressedversion of the image; a decompressor element configured to performinverse DCTs (IDCTs) and inverse DQTs (IDQTs) the compressed version ofthe image; a determiner element configured to determine the differencesbetween the image and the decompressed version of the image therebycreating a residual version of the image; and a combiner elementconfigured to combine the lossy version of the image and the residualversion of the image, wherein the combination of the lossy version ofthe image and the residual version of the image is substantially thesame as the original image.